We are most appreciative of the feedback we’ve received, through the blog and elsewhere, on NIH support of model organism research. In part 1 of this series, we mentioned that we asked two separate groups to analyze NIH applications and awards. In parts 1 and 2 we primarily focused on R01-based data that were curated and analyzed by our Office of Portfolio Analysis. In part 3, we show results from a broader range of research project grant (RPG) data that were prepared and analyzed by our Office of Research Information Systems. This group used an automated thesaurus-based text mining system which delves into not only public data such as project titles, abstracts, public health relevance statements, but also the specific aims contained in RPG applications. …. Continue reading
We were pleased to hear the feedback on our previous post on NIH-funded model-organism research. One question a number of you asked is: what’s happening with research involving mouse models? Thanks to additional work by colleagues in NIH’s Office of … Continue reading
Wangler, et al. recently published an article in Genetics on NIH funding for model organism research involving Drosophila. The authors extracted grant information from NIH ExPORTER and looked for the word “Drosophila” in either the title or abstract. By this approach the authors found that NIH support for Drosophila-based research is declining.
We chose to investigate further trends in NIH support for Drosophila and other model organism research. Two groups of NIH staff used two different approaches. Our Office of Research Information Systems (ORIS) used an automated thesaurus-based text mining system which mines not only project titles and abstracts but also the specific aims contained in the application; this is the system we use to generate “Research Condition and Disease Category” (or RCDC) tables, which are publicly posted to the NIH RePORT website. In a separate effort, our Office of Portfolio Analysis (OPA) supplemented a different text mining algorithm with extensive manual curation. Both methods – the wholly automated thesaurus-based text mining approach and the manual curation supplemented text mining approach – yielded similar findings. In this blog, we will present the results of the manually curated approach. …. Continue reading